How To Build A Chat Bot That Learns From The User In Python Tutorial
TLDRIn this tutorial, viewers learn how to create a Python chatbot that can learn from user interactions. The bot's uniqueness lies in its ability to remember responses taught by users and adapt its replies accordingly. The process involves creating a JSON knowledge base to store questions and answers. As users converse with the bot, it uses the 'difflib' library to find the closest match to the user's input and respond. If the bot doesn't understand, it prompts the user to teach it a new response, which is then added to the knowledge base. This iterative learning process allows the bot to improve over time. The tutorial also provides a GitHub link for those who wish to delve deeper or need help with the code. The chatbot is a practical project for Python learners to grasp the basics and understand how to work with JSON files and string manipulation.
Takeaways
- 📝 The tutorial is about creating a chatbot in Python that learns from user input.
- 💡 The chatbot's code was initially generated by GPT4 and then modified for the tutorial.
- 🔧 The bot improves its responses over time by storing learned interactions in a JSON file.
- 📚 The project serves as a practical way to learn Python basics, especially for beginners.
- 🔗 A GitHub repository link is provided for those who want to access the code directly.
- 📁 A 'knowledge_base.json' file is created to store the bot's questions and responses.
- 🤖 The bot uses the 'difflib' library to find the closest match for user inputs.
- 📈 The 'get_close_matches' function helps in retrieving the most relevant response with a specified accuracy level.
- 🔄 The 'save_knowledge_base' function updates the JSON file with new responses after each interaction.
- 🚀 The chatbot operates in an infinite loop, allowing continuous conversation and learning.
- ✅ Users can teach the bot new responses, which are then saved and remembered for future use.
Q & A
What is special about the chatbot being discussed?
-The chatbot is special because it learns from user inputs and can remember responses from previous chats, storing them in a JSON file.
How does the chatbot learn from user interactions?
-The chatbot learns by receiving user inputs and being taught responses. These responses are then stored in a JSON file for future use.
What is the purpose of the 'knowledge_base.json' file?
-The 'knowledge_base.json' file serves as the storage for the chatbot's learned responses, allowing it to recall them in subsequent conversations.
How does the chatbot match user inputs to responses?
-The chatbot uses the 'difflib.get_close_matches' function to find the closest match to the user's input from its knowledge base.
What is the significance of the 'cutoff' parameter in 'get_close_matches'?
-The 'cutoff' parameter determines the minimum level of similarity required for a response to be considered a match, ensuring a certain level of accuracy in the chatbot's responses.
How can users teach the chatbot a new response?
-Users can teach the chatbot by typing a response when prompted after the chatbot indicates it does not know the answer.
What happens when the chatbot encounters an input it doesn't understand?
-The chatbot will ask the user to teach it a response, which it will then store in its knowledge base for future reference.
How is the chatbot's knowledge base updated?
-The knowledge base is updated by appending new user-inputted questions and answers to the JSON file and then saving the changes.
Why is there a need for a 'save_knowledge_base' function?
-The 'save_knowledge_base' function is necessary to persist the chatbot's learned responses across different program runs.
What is the role of the 'load_knowledge_base' function?
-The 'load_knowledge_base' function is used to load the chatbot's existing knowledge from the JSON file when the program starts.
How does the chatbot handle multiple potential matches for a user input?
-The chatbot uses the 'get_close_matches' function with 'n=1' to return only the best match, ensuring the most relevant response is given.
What programming environment is recommended for this project?
-The script mentions using Python 3.11 in PyCharm, but any code editor that supports Python can be used, such as Visual Studio Code.
Outlines
🤖 Creating a Learning Chatbot in Python
This paragraph introduces the project of building a chatbot in Python that can learn from user interactions. The chatbot is unique because it was generated with the help of GPT4 and can be modified to suit the tutorial's needs. The bot's ability to learn is demonstrated through a scenario where it responds to the user's greeting 'hello' with a taught response 'hey there'. The learning process involves adding responses to a JSON file that the bot will refer to for future interactions. The video also provides a link to a GitHub repository for those who wish to follow along with the code.
📚 Building the Knowledge Base and Functions
The second paragraph details the process of setting up the chatbot's knowledge base and the necessary functions for its operation. It involves creating a JSON file named 'knowledge_base.json' and populating it with questions and answers. The script includes importing necessary modules, loading the knowledge base from the JSON file, and saving new responses back to the file. It also outlines creating a function to find the best match for a user's question from the knowledge base and another function to retrieve the answer for a given question. The paragraph concludes with the setup for the main chatbot script, which includes an infinite loop for continuous user interaction, a mechanism for exiting the program, and logic for teaching the bot new responses.
🚀 Running the Chatbot and Training It
The final paragraph demonstrates how to run the chatbot and how it learns from user input. It explains that when the chatbot encounters an unknown input, it asks the user for a response which it then adds to its knowledge base. The bot's ability to remember previous responses is showcased by rerunning the program and showing that it can recall and use the taught responses. The video concludes with a mention of the chatbot's potential for growth through continuous interaction and learning, and a thank you note to the viewers.
Mindmap
Keywords
💡Chatbot
💡Python
💡GPT4
💡Json file
💡Knowledge base
💡Learning from user input
💡Difflib
💡Code editor
💡GitHub repository
💡List comprehension
💡Infinite loop
💡Exit condition
Highlights
The video tutorial teaches how to create a chatbot in Python that learns from user input.
The chatbot was initially generated by GPT4 and modified for the tutorial.
The bot can learn new responses when provided with a teaching input.
The chatbot uses a JSON file to store and retrieve learned responses.
The knowledge base is loaded from a JSON file at the start of the program.
A function called 'save_knowledge_base' is used to update the JSON file with new responses.
The 'find_best_match' function uses difflib to find the closest match for user input.
A cutoff value is used to determine the accuracy of the best match response.
The 'get_answer_for_question' function retrieves the answer from the knowledge base.
An infinite loop is used to continuously interact with the chatbot.
The user can exit the chatbot by typing 'quit'.
If the bot doesn't know the answer, it asks the user to teach it.
New answers are added to the knowledge base and saved to the JSON file.
The chatbot can remember and use previously learned responses even after being rerun.
The tutorial includes a GitHub repository link for accessing the code.
The chatbot project is suitable for beginners learning Python to understand basic programming concepts.
The video provides a step-by-step guide on building and training the chatbot.
The chatbot can be trained with various responses to improve its learning capabilities.
The knowledge base is updated in real-time as the user teaches the bot new responses.